Research Projects of [M]ESML

Land, water, vegetation, and exchanges of water/energy between land and the atmosphere.

 

Remote Sensing of Water & Vegetation

We monitor key parameters of water, land, and vegetation using a combination of aerial/satellite sensors in optical, thermal and microwave ranges from paddock to global scales

Land-Atmosphere Interactions

Land, atmosphere, and ocean interact via complex exchanges of water, energy, and other material. We explore how changes in land cover and irrigation trigger chain reactions and influence the large-scale water cycle and water-dependent human life

 

Hydrological Modelling

We investigate some of the long-standing challenges in hydrological research, e.g., model calibration in ungauged basins and water quality prediction, using innovative ideas combined with new monitoring tools

Data Assimilation

With a plethora of 'measurement' methods and data, earth observation systems have become an integral part of environmental prediction models. We investigate methods to optimally integrate observations of various sources and supports into hydrological and biophysical models

 

Plant Water Stress Mapping for Precision Agriculture Using UAV-borne Thermography

  • Developing an automated method to interpret high-resolution imagery to map tree-by-tree water stress levels using near-real-time UAV-borne sensing
  • Providing a feasible method of plant water stress mapping and at the plant scale as precision irrigation purposes
  • Investigating cultivar-specific and phenological stages-specific metrics
  • Kate Park (PhD scholar), Dr. Sigfredo Fuentes (collaborator, co-supervisor, FVAS, UoM), Dr. Hoam Chung (collaborator, co-supervisor, Monash University), Dr. Mark O'Connell (collaborator, DEDJTR)
 

Figure 1. Water stress map of a citrus field in Mildura, Victoria, Australia. This project is funded by Hort-Eye Pty Ltd.

 

Multi-sensor Remote Sensing of Crop Nutrients and Water Stress 

  • In order to materialize the concept of precision agriculture, remotely sensed data is being used in conjunction with field sensor data. Remote sensing data provide very high and flexible spatial and temporal resolution along with non-destructive and low cost data collection. The remote sensing data has been proved to be in the close approximation of crop growth deciding parameters. Canopy moisture and nitrogen content are the most dominant parameters limiting the yield of the crops. Canopy nitrogen and moisture prediction with high accuracy and spatial resolution will play a key role in the efficient use of resources to the farm. Creating canopy level moisture and nitrogen content map at high spatial and temporal resolution will greatly assist in site-specific irrigation and fertilizer application. This will lead to economic and ecological benefits in agriculture in terms of fertilizer cost reduction, high water use efficiency and less nitrate leaching in the groundwater system. 
  • Manish K. Patel (PhD scholar), Prof. Andrew W. Western (Co-supervisor, UoM), Prof. Iain Young (Co-Supervisor, University of Sydney)

[LEFT] Differential hyperspectral pasture nutrient index estimated treatment plot by plot; [ABOVE] Experimental site overview. The experiment is funded by the Australia-China Joint Research Centre fund and assisted by MUASIP

Hyperspectral sensing of pasture and soil nitrogen at an experimental site in Warrnambool, Victoria, Australia. The site is managed by Dr. Helen C. Suter of Agricultural Sciences at The University of Melbourne and Graeme Ward. 

Watering Here, Raining There: Our Fingerprints on Land and Atmosphere

  • This project aims to reveal the possible impacts of anthropogenic activities (such as the agricultural irrigation, land use and land cover change, aerosols, etc.) on the regional climate and the feedback mechanisms behind. For instance, significant water vapor export from the agricultural irrigation exploitation could change the local water/energy balance and regional atmospheric circulation. India experienced the agricultural irrigation development from the 1950s, and there was an observed reduction of Indian Summer Monsoon Rainfall during the second half of twentieth century. So far, what we found is that the agricultural irrigation weakened the Indian Summer Monsoon winds (see the figure) and reduced rainfall with the high temporal and spatial variabilities. Now we are looking at the influences of other anthropogenic activities on the regional climate and the interactions among them.
  • Chihchung Chou (PhD scholar), Prof. Min-Hui Lo (Co-supervisor, National Taiwan University, Taiwan), Prof. Hector M. Malano (Co-supervisor)
 
MESML_graph1.jpg
 
MESML_graph2.jpg

Investigation of sustainable national water resources management of India in a changing climate

  • Freshwater scarcity and unsustainable water use is a growing concern in many developing countries including India. Increasing water demand coupled with rainfall variability associated with climate change exacerbates water scarcity. The increase in water demand is attributed to population and economic growth as well as technological change, and the gap between the actual water available and the demand continues to widen. This study aims to develop a large-scale assessment model of sustainable water use in India during the period 1970 - 2010 at a spatial resolution of 50 km x 50 km and a temporal resolution of monthly timescale. 
  • A Community Land Model CLM 4.0, developed by the National Centre for Atmospheric Research (NCAR) of the US, and census-based statistical database are used in this study to quantify and assess the sustainable water use in India. We define the measure of sustainable water use as the difference between the total water available and the total water demand. For each grid cell, the total water available is modelled as the sum of surface runoff and groundwater. The total water demand is estimated as the sum of irrigation, industrial, domestic and environmental water demand in each grid cell. Among the demands, the irrigation water demand is modelled based on census data sets of irrigated areas and irrigation water withdrawal while the domestic and industrial water demand is modelled as a function of population, economic and technological indicators such as gross domestic product, electricity, fuel consumption and industrial outputs.
  • Naveen Joseph (PhD scholar), Prof. Hector M. Malano (Co-supervisor), Dr. Biju George (Co-supervisor, Bureau of Meteorology), Prof. K. P. Sudheer (Co-supervisor, IIT Madras, India) 
 
 Figure 1. Spatial map of population affected by water scarcity in India at monthly time-scale

Figure 1. Spatial map of population affected by water scarcity in India at monthly time-scale

 

Analysis and prediction of stream water quality in the Great Barrier Reef catchments using Bayesian hierarchical model

  • The Great Barrier Reef (GBR) is the world's largest coral ecosystem and it has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The land-derived pollutants are responsible for the degradation of instream water quality in the GBR catchment. The spatial and temporal variations in water quality hinder the interpretation of the water quality monitoring data. The water quality monitoring program provides a potential opportunity to develop a data-driven understanding of water quality at catchment scale; including both natural and anthropogenic influences on water quality. In this project, we investigated water quality monitoring records from 32 site across the GBR catchments (Figure 1). We adopted Bayesian hierarchical modelling: decomposes the complex interactions in the observed data into a series of conditional models. We incorporated the spatial variability in stream water quality within a two-level modelling structure to capture the variability between sites (Figure 2). The modelling results (Figure 3) indicated large positive deviation: Burdekin and Fitzroy sites, especially 3 upland sites in Fitzroy.  The inference of the model parameters (Figure 4) showed that: 1) land use - grazing and dry land agriculture have significant impacts on source of TSS; 2)   geology: soil erodibility - impact on pollutant mobilisation (erosion effect); 3) topography: slope - impact on pollutant delivery.
  • Shuci Liu (PhD scholar), Prof. Andrew W. Western (Co-supervisor), Dr. Anna Lintern (Co-supervisor, Monash), Dr. Angus Webb (Co-supervisor), Dr. David Waters (Collaborator)
 
Picture1.jpg
Shuci_Fig2.jpg
 
 
Picture3.jpg
Picture4.jpg

Hydrological model calibration in the ungauged basins using ground-based and satellite observations of river stages

  • Jie Jian (PhD Scholar), Dr. Justin Costelloe (Co-supervisor), Prof. QJ Wang (Co-supervisor)

Introduction

  • Hydrological modelling is used as a tool to understand and quantify hydrological processes and is applied in predictions and decision-making processes. Model parameters that cannot be measured directly should be calibrated to make models accurate. Thus, the observed discharge data that can be used to constrain the model parameters are essential. However, the majority of the streams in the world are ungauged or sparsely gauged. In these catchments, the uncertainty issue is quite obvious and significant due to the lack of continuous discharge data. Thus new calibration methods that could use water level data instead are crucial in streamflow prediction in ungauged and poorly gauged areas. 

The potential to use water level data

  • There is an obvious positively monotonic relationship between discharge (Q) and water level (h) in most natural rivers;
  • Water level data is available in a significant number of catchments that lack rated discharge;
  • The availability of altimetry data is likely to provide h to many ungauged catchments.

Methods

  • Two calibration schemes, which are not reliant on extensive observed discharge data, are examined: 
  • Scheme 1: Spearman Rank correlation based scheme (SRC);
  • Scheme 2: Inverse Rating Curve based scheme (IRC).
  • A small number of discharge measurements (50th, 75th and 95th percentiles of Q_obs) and some regionalised runoff ratio values are used to constrain the model parameters.

Results and discussions

  • SRC reproduced the dynamics of flow events but contained large biases, because it did not contain the information on the dynamic range of the discharge observations (Figure 1b);
  • IRC performed better because it provided stronger relationship between Q and h (Figure 1c);
  • SRC/IRC with a small number of high flow Q_obs could effectively improve the calibration performances (Figure 2).

Ongoing works

  • The study is extended to 200+ Hydrological Reference Stations in Australia;
  • More hydrological models are tested, such as SIMHYD, GR4J, AWBM, IHACRES and SACSMA;
  • The influences of different hydro-climatic and catchment variables are examined;
  • Instead of high flow data, other effective factors are explored to constrain the model performances.
 Figure 1. Hydrographs of the control case, SRC case and IRC case

Figure 1. Hydrographs of the control case, SRC case and IRC case

 
 Figure 2. Difference between Q_est and Q_obs in FDCs

Figure 2. Difference between Q_est and Q_obs in FDCs

 

Under construction

 
  • Lag-aware streamflow assimilation
  • Dual data assimilation for discharge prediction
  • Multi-satellite assimilation of soil and vegetation states into a crop model